The aim of this study was to investigate the relationship between image patterns in cephalometric radiographs and the diagnosis of orthognathic surgery and propose a method to improve the accuracy of predictive models according to the depth of the neural networks. The study included 640 and 320 patients requiring non-surgical and surgical orthodontic treatments, respectively. The data of 150 patients were exclusively classified as a test set. The data of the remaining 810 patients were split into five groups and a five-fold cross-validation was performed. The convolutional neural network models used were ResNet-18, 34, 50, and 101. The number in the model name represents the difference in the depth of the blocks that constitute the model. The accuracy, sensitivity, and specificity of each model were estimated and compared. The average success rate in the test set for the ResNet-18, 34, 50, and 101 was 93.80%, 93.60%, 91.13%, and 91.33%, respectively. In screening, ResNet-18 had the best performance with an area under the curve of 0.979, followed by ResNets-34, 50, and 101 at 0.974, 0.945, and 0.944, respectively. This study suggests the required characteristics of the structure of an artificial intelligence model for decision-making based on medical images.

The aim of this study was to evaluate the effects of the tongue and lip pressure on dentofacial morphology. The subjects comprised 194 patients with malocclusion. Anterior and posterior tongue elevation and lip pressures were evaluated using the Iowa Oral Performance Instrument (IOPI) device. The lateral cephalograms of each subject were traced and digitized to perform the analysis. Statistical analysis was used to investigate the relationship between perioral muscle force and the cephalometric variables. Anterior and posterior tongue pressure was both higher in males than in females. No sex difference in lip pressure was observed. The group with a low posterior tongue pressure showed a short ramus height, short posterior facial height, and clockwise-rotated mandible. On the other hand, lip pressure had a significant influence on maxillary incisor angulation. Skeletal pattern was not found to be significantly related with lip pressure. The anterior tongue pressure appeared as a mixed pattern of the two results. Tongue pressure was related to skeletal measurements, such as short posterior facial height, and lip pressure was related to the angulation of the anterior teeth. This study suggests that there may be differences in dentofacial morphology according to the differences in perioral muscle force.

Objective: To evaluate three-dimensional (3D) positional changes of an intruded tooth, a neighboring tooth, and a tooth connected to a mini-implant following intrusion of a single supraerupted molar, using a mini-implant with partial-fixed orthodontic appliances. Materials and Methods: The study consisted of 14 adult patients (two males and 12 females, mean age 41.9 years) with a supraerupted molar due to loss of an antagonist. Intrusion was performed using a mini-implant with a partial strap-up. The mean treatment time was 11.9 months, and the mean retention time was 23.3 months. To quantify the positional changes of the teeth, 3D models using a laser-based, dental scanning system and 3D software at pretreatment, posttreatment, and retention were oriented in a coordinate system and superimposed using nonmoved teeth as references. The changes on the x-, y-, and z-axes were measured at the tip of each cusp in the involved teeth. Results: A supraerupted molar was intruded by a mean amount of 1.35 ± 0.48 mm and was well maintained during the retention period. The overall change in the neighboring tooth was insignificant, although it showed opposite movement compared to the intruded tooth during the intrusion. The tooth connected to a mini-implant exhibited a secure anchorage. Conclusion: 3D analysis showed the detailed positional changes of each tooth, and the involved molars were well maintained after intrusion.

Rationale: Tissue engineering through three-dimensional (3D) bioprinting has emerged as a highly promising strategy for creating custom-designed 3D bioconstructs that closely mimic native tissue architecture. However, ongoing advancements in bioink formulation and bioprinting processes are required to achieve precise replication of target tissues. In particular, effective vascularization and extracellular remodeling are essential for successful gingival tissue regeneration.